01_regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Importing plotly failed. Interactive plots will not work.\n"
     ]
    }
   ],
   "source": [
    "#hide\n",
    "import datetime\n",
    "import logging\n",
    "import sys\n",
    "\n",
    "from fbprophet import Prophet\n",
    "from loguru import logger\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas.core.frame import DataFrame\n",
    "from pandas._libs.tslibs.timestamps import Timestamp\n",
    "import seaborn as sns\n",
    "import statsmodels.stats.api as sms\n",
    "from statsmodels.graphics.tsaplots import plot_acf\n",
    "from statsmodels.tools.tools import add_constant\n",
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "\n",
    "import elec_consumption.forecast as forecast\n",
    "import elec_consumption.utils as utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hide\n",
    "%matplotlib inline\n",
    "plt.style.use('seaborn')\n",
    "_golden_ratio = 1.61803398875\n",
    "plt.rcParams['figure.figsize'] = [6 * _golden_ratio, 6]\n",
    "\n",
    "logging.disable(sys.maxsize)\n",
    "\n",
    "data = {\n",
    "    'zeroHour': None,  # series, hourly power profile of unit 0\n",
    "    'zeroHourFb': None,  # for Prophet, hourly power profile of unit 0\n",
    "}  # contain modified dataframes\n",
    "mod = {\n",
    "    'zeroDayFb': None,  # for Prophet using daily power profile of unit 0\n",
    "    'zeroHourFb': None,  # Prophet, using hourly power profile of unit 0\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hide\n",
    "# Import 500 profiles.\n",
    "df = pd.read_csv('./data/raw.csv', index_col=0)\n",
    "df.columns = list(range(df.shape[1]))\n",
    "df.columns.name = 'household'\n",
    "df.index = pd.to_datetime(df.index)\n",
    "df.index.name = 'datetime'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Introduction\n",
    "\n",
    "Methods for two sets of forecasts, using hourly and daily profiles respectively, are discussed here. Profiles are modelled independently for now.\n",
    "\n",
    "### Why not Multivariate Time Series\n",
    "\n",
    "It is known that there is no relationship between profiles from different households, because their behaviours are not directly correlated. The only regressor considered should be time. Others like temperature may contribute, but are unknown for now. So it is not necessary to use vector auto regression (VAR). \n",
    "\n",
    "### Why Facebook `Prophet`\n",
    "\n",
    "[`Prophet`](https://facebook.github.io/prophet/) is an open-source package for modelling and forecasting univariate time series by Facebook. Its model structure is similar to that of generalised additive models (GAM), where distinct non-linear terms are integrated in a linear way. `Prophet` works best when the series shows strong and multiple seasonality, and it is robust to missing values and effect from holidays, making it the perfect choice in our case."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Demo: Daily Profile of Household 0\n",
    "\n",
    "Though profiles in the same cluster tend to show similar patterns, it is relatively easy to build different statistical model for different households. Daily profiles of household 0 is modelled by Facebook `Prophet` and seasonal ARIMA in this section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hide\n",
    "# To pandas time series\n",
    "data['tsPdDaily'] = df.iloc[:, 0].resample('1D', closed='left').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Facebook `Prophet`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hide\n",
    "ts_fb = data['tsPdDaily'].to_frame().reset_index()  # to Prophet time series\n",
    "ts_fb.columns = ['ds', 'y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<fbprophet.forecaster.Prophet at 0x127f1f890>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide\n",
    "mod['zeroDayFb'] = Prophet()\n",
    "mod['zeroDayFb'].fit(ts_fb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hide\n",
    "df_future = mod['zeroDayFb'].make_future_dataframe(periods=3)\n",
    "df_forecast = mod['zeroDayFb'].predict(df_future)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Point forecasts and confidence intervals are plotted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#hide_input\n",
    "mod['zeroDayFb'].plot(\n",
    "    df_forecast, xlabel='date', ylabel='daily power consumption speed (kW)'\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Forcasts and confidence intervals for future 3 days:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>yhat</th>\n",
       "      <th>yhat_lower</th>\n",
       "      <th>yhat_upper</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-12-01</th>\n",
       "      <td>0.135803</td>\n",
       "      <td>0.085666</td>\n",
       "      <td>0.183257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-02</th>\n",
       "      <td>0.159741</td>\n",
       "      <td>0.110511</td>\n",
       "      <td>0.206479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-03</th>\n",
       "      <td>0.154350</td>\n",
       "      <td>0.108852</td>\n",
       "      <td>0.201318</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                yhat  yhat_lower  yhat_upper\n",
       "date                                        \n",
       "2017-12-01  0.135803    0.085666    0.183257\n",
       "2017-12-02  0.159741    0.110511    0.206479\n",
       "2017-12-03  0.154350    0.108852    0.201318"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide_input\n",
    "res = df_forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail(3)\n",
    "res['ds'] = res['ds'].apply(lambda x: x.date())\n",
    "res = res.set_index('ds')\n",
    "res.index.name = 'date'\n",
    "res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the previous figure, there is a trending and a weekly seasonal components, which are shown in following two figures respectively. The trending component increased from 0.104 to 0.141 in 122 days. That is, the average power consumption increased by 34.93 % in merely 4 months. Such trending effect must be taken into account.\n",
    "\n",
    "Surprisingly, there are variations in power consumption over one week. Household 0 tends to consume more during weekends and Thursday. The difference between values on Saturday and on Wednesday amounts to 28.98 % of the mean power consumption, so this effect is essential as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#hide_input\n",
    "mod['zeroDayFb'].plot_components(df_forecast);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(34.932460347034294, 0.14090571916533756, 0.10442685088742959)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide\n",
    "utils.cal_increment_percent(df_forecast['trend'][:122])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>111.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.122169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.040575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.039063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.104333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.114188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.134198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.325417</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                0\n",
       "count  111.000000\n",
       "mean     0.122169\n",
       "std      0.040575\n",
       "min      0.039063\n",
       "25%      0.104333\n",
       "50%      0.114188\n",
       "75%      0.134198\n",
       "max      0.325417"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide\n",
    "data['tsPdDaily'].dropna().describe().to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.98089486763557"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide\n",
    "_, _max, _min = utils.cal_increment_percent(df_forecast.head(7)['weekly'][:122])\n",
    "(_max - _min) / 0.122169 * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Residual analysis for this `Prophet` model has not been finished. Some of the techniques are demonstrated in specification tests and autocorrelation tests in section 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hide\n",
    "data['resDaily'] = df_forecast.loc[:121, ['ds', 'yhat']]\n",
    "data['resDaily'].set_index('ds', inplace=True)\n",
    "data['resDaily']['yhat'] = data['resDaily']['yhat'] - data['tsPdDaily']\n",
    "data['resDaily'].columns = ['resid']\n",
    "data['resDaily']['resid'].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 698.991x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#hide\n",
    "_, ax = plt.subplots(1, 1)\n",
    "data['resDaily'].reset_index().plot.scatter(x='ds', y='resid', ax=ax);\n",
    "ax.axhline(0, ls='--', color='r');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 698.991x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#hide\n",
    "utils.plot_acf_pacf(data['resDaily'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seasonal ARIMA\n",
    "\n",
    "It is impossible to plot ACF and PACF for original series because of missing entries. As discussed before, they are adjacent so it is hard to fill with meaningful values. Previous result provides a starting point. To have a weekly season is a good idea, and it should be \"integrated\", in order to remove the long-term trend."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For example, an integrated SARIMA (with AR1, MA1 and weekly seasonal AR1) can be built. The seasonal AR1 term (value 0.1603, std error 0.145, pvalue 0.269) is a bit controversial, but stays due to the previous finding. The normality and heteroskedasticity assumptions are both rejected, so this is not good model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hide\n",
    "fit = ARIMA(\n",
    "    endog=data['tsPdDaily'], order=(1, 1, 1), freq='D',\n",
    "    seasonal_order=(1, 0, 0, 7)\n",
    ").fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>                 <td>y</td>              <th>  No. Observations:  </th>    <td>122</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>ARIMA(1, 1, 1)x(1, 0, [], 7)</td> <th>  Log Likelihood     </th>  <td>204.958</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                  <td>Fri, 18 Dec 2020</td>       <th>  AIC                </th> <td>-401.915</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                      <td>10:23:45</td>           <th>  BIC                </th> <td>-390.732</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                   <td>08-01-2017</td>          <th>  HQIC               </th> <td>-397.373</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                         <td>- 11-30-2017</td>         <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>              <td>opg</td>             <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>   <td>    0.4273</td> <td>    0.096</td> <td>    4.450</td> <td> 0.000</td> <td>    0.239</td> <td>    0.615</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>   <td>   -0.9926</td> <td>    0.082</td> <td>  -12.091</td> <td> 0.000</td> <td>   -1.154</td> <td>   -0.832</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.S.L7</th> <td>    0.1603</td> <td>    0.145</td> <td>    1.105</td> <td> 0.269</td> <td>   -0.124</td> <td>    0.445</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>  <td>    0.0013</td> <td>    0.000</td> <td>   11.158</td> <td> 0.000</td> <td>    0.001</td> <td>    0.002</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.44</td> <th>  Jarque-Bera (JB):  </th> <td>122.13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.51</td> <th>  Prob(JB):          </th>  <td>0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>2.71</td> <th>  Skew:              </th>  <td>1.49</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td> <th>  Kurtosis:          </th>  <td>6.91</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                    SARIMAX Results                                     \n",
       "========================================================================================\n",
       "Dep. Variable:                                y   No. Observations:                  122\n",
       "Model:             ARIMA(1, 1, 1)x(1, 0, [], 7)   Log Likelihood                 204.958\n",
       "Date:                          Fri, 18 Dec 2020   AIC                           -401.915\n",
       "Time:                                  10:23:45   BIC                           -390.732\n",
       "Sample:                              08-01-2017   HQIC                          -397.373\n",
       "                                   - 11-30-2017                                         \n",
       "Covariance Type:                            opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ar.L1          0.4273      0.096      4.450      0.000       0.239       0.615\n",
       "ma.L1         -0.9926      0.082    -12.091      0.000      -1.154      -0.832\n",
       "ar.S.L7        0.1603      0.145      1.105      0.269      -0.124       0.445\n",
       "sigma2         0.0013      0.000     11.158      0.000       0.001       0.002\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.44   Jarque-Bera (JB):               122.13\n",
       "Prob(Q):                              0.51   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               2.71   Skew:                             1.49\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                         6.91\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide\n",
    "fit.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Good news is that there is nothing left according to ACF and PACF. This example is actually the final model selected after a manual model selection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 698.991x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#hide_input\n",
    "utils.plot_acf_pacf(fit.resid.fillna(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pseudo Out-of-Sample Validation\n",
    "\n",
    "It is challenging to conduct a cross validation for time series models, especially when there is long-term trend. Pseudo out-of-sample validation is used instead. Here is a [description from `statsmodels`](https://www.statsmodels.org/stable/examples/notebooks/generated/statespace_forecasting.html#Cross-validation):\n",
    "\n",
    "\"A common use case is to cross-validate forecasting methods by performing h-step-ahead forecasts recursively using the following process\"\n",
    "\n",
    "- Fit model parameters on a training sample\n",
    "- Produce h-step-ahead forecasts from the end of that sample\n",
    "- Compare forecasts against test dataset to compute error rate\n",
    "- Expand the sample to include the next observation, and repeat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MSE of 3-step forecast using Facebook Prophet is 0.00356, and its root is 48.83 % of mean power consumption. So this is not a good model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hide\n",
    "frames = []\n",
    "for i in range(97, 119):\n",
    "    frames.append(\n",
    "        forecast.validate(forecast.fit_prophet, data['tsPdDaily'], i)\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.003558756357791278"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide\n",
    "# Result is 0.003558756357791278.\n",
    "res = pd.concat(frames)\n",
    "mse = (res['resid']**2).mean()\n",
    "mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48.830155690053715"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide\n",
    "np.sqrt(mse) / 0.122169 * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MSE of 3-step forecast using seasonal ARIMA is 0.00370, which is 3.94 % higher than MSE using Prophet, and its root is 49.78 % of mean power consumption. Despite of the increment, this model structure will be used to model all other daily profiles. Two approaches are essential the same. It is easier to interpret seasonal ARIMA, but it cannot be used for hourly profiles, which is discussed in the following section. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hide\n",
    "from importlib import reload\n",
    "reload(forecast)\n",
    "\n",
    "frames = []\n",
    "for i in range(97, 119):\n",
    "    frames.append(\n",
    "        forecast.validate(forecast.fit_sarima, data['tsPdDaily'], i)\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.003698980432964265"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide\n",
    "# Result is 0.003834059794252662.\n",
    "res = pd.concat(frames)\n",
    "mse = (res['resid']**2).mean()\n",
    "mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.94025499570912"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide\n",
    "(0.003698980432964265 - 0.003558756357791278) / 0.003558756357791278 * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.49782877764526706"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide\n",
    "np.sqrt(mse) / 0.122169"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Demo: Hourly Profile of Household 0\n",
    "\n",
    "`Prophet` can even be used to model time series with hourly (sub-daily) resolution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<fbprophet.forecaster.Prophet at 0x12a6e12d0>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide\n",
    "data['zeroHour'] = df.iloc[:, 0].resample('1H', closed='left').mean()  # to pandas time series\n",
    "data['zeroHourFb'] = data['zeroHour'].to_frame().reset_index()  # to Prophet time series\n",
    "data['zeroHourFb'].columns = ['ds', 'y']\n",
    "mod['zeroHourFb'] = Prophet()\n",
    "mod['zeroHourFb'].fit(data['zeroHourFb'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hide\n",
    "df_future = mod['zeroHourFb'].make_future_dataframe(\n",
    "    periods=24, freq='H', include_history=True\n",
    ")\n",
    "df_forecast = mod['zeroHourFb'].predict(df_future)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hide\n",
    "# Store endogenous variables, in-sample predictions, and residuals in one dataframe.\n",
    "data['zeroHourFbResid'] = df_forecast.loc[\n",
    "    df_forecast['ds'] < Timestamp('2017-12-01 00:00:00'),\n",
    "    ['ds', 'yhat']\n",
    "]\n",
    "data['zeroHourFbResid'].set_index('ds', inplace=True)\n",
    "data['zeroHourFbResid'].index.name = 'datetime'\n",
    "data['zeroHourFbResid']['y'] = data['zeroHour']\n",
    "data['zeroHourFbResid']['resid'] = data['zeroHourFbResid']['y'] - data['zeroHourFbResid']['yhat']\n",
    "\n",
    "# Drop rows with NaN.\n",
    "data['zeroHourFbResidNaN0'] = data['zeroHourFbResid'].dropna()\n",
    "assert data['zeroHourFbResidNaN0'].shape == (2664, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a line plot for in-sample and out-of-sample forecasts and confidence intervals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 698.991x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#hide_input\n",
    "_, ax = plt.subplots(1, 1)\n",
    "ax.set_xlim([Timestamp('2017-11-29 06:00:00'), Timestamp('2017-12-02 00:00:00')])\n",
    "ax.set_ylim([-0.2, 0.6])\n",
    "mod['zeroHourFb'].plot(\n",
    "    df_forecast, ax=ax, xlabel='date', ylabel='daily power consumption speed (kW)'\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Out-of-sample forecasts for future 4 hours are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>yhat</th>\n",
       "      <th>yhat_lower</th>\n",
       "      <th>yhat_upper</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-12-01 00:00:00</th>\n",
       "      <td>0.157450</td>\n",
       "      <td>0.026090</td>\n",
       "      <td>0.290846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-01 01:00:00</th>\n",
       "      <td>0.135131</td>\n",
       "      <td>-0.005102</td>\n",
       "      <td>0.282784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-01 02:00:00</th>\n",
       "      <td>0.115922</td>\n",
       "      <td>-0.021668</td>\n",
       "      <td>0.253831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-01 03:00:00</th>\n",
       "      <td>0.108154</td>\n",
       "      <td>-0.024035</td>\n",
       "      <td>0.245774</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         yhat  yhat_lower  yhat_upper\n",
       "datetime                                             \n",
       "2017-12-01 00:00:00  0.157450    0.026090    0.290846\n",
       "2017-12-01 01:00:00  0.135131   -0.005102    0.282784\n",
       "2017-12-01 02:00:00  0.115922   -0.021668    0.253831\n",
       "2017-12-01 03:00:00  0.108154   -0.024035    0.245774"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide_input\n",
    "res = df_forecast.loc[2928:2931, ['ds', 'yhat', 'yhat_lower', 'yhat_upper']]\n",
    "res.set_index('ds', inplace=True)\n",
    "res.index.name = 'datetime'\n",
    "res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multiple Seasonality\n",
    "\n",
    "Here are plots for one trending and two seasonality components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#hide_input\n",
    "mod['zeroHourFb'].plot_components(df_forecast);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately, it is impossible to apply seasonal ARIMA, because of the multiple seasonality."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Specification Tests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The assumption that residuals from Prophet model are normally distributed is rejected. Furthermore, according to White's test for Heteroscedasticity, variance of residuals does vary with power consumptions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide\n",
    "sms.jarque_bera(data['zeroHourFbResid']['resid'])[1] < 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide\n",
    "sms.het_white(\n",
    "    data['zeroHourFbResidNaN0']['resid'],\n",
    "    add_constant(data['zeroHourFbResidNaN0']['y'])\n",
    ")[1] < 0.05"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When power consumption is high, the in-sample forcast is expected to have a large deviance. The same should go for out-of-sample forecasts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 698.991x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#hide_input\n",
    "ax = data['zeroHourFbResid'].plot(x='y', y='resid', kind='scatter');\n",
    "ax.set_ylabel(\"residual (kW) from Facebook Prophet\");\n",
    "ax.set_xlabel(\"hourly power consumption (kW)\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In spite of the previous finding, when residuals are compared to corresponding values, they can be 4 times larger. So it is hard to say that more precise forecasts can be made when power consumption is low (usually during nights)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 698.991x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#hide\n",
    "data['zeroHourFbResid']['residAbsRelative'] = (\n",
    "    data['zeroHourFbResid']['resid'].abs() /\n",
    "    data['zeroHourFbResid']['y']\n",
    ")\n",
    "\n",
    "ax = data['zeroHourFbResid'].plot(x='y', y='residAbsRelative', kind='scatter');\n",
    "ax.set_ylabel(\"abs of residual / power\");\n",
    "ax.set_xlabel(\"hourly power consumption (kW)\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Autocorrelation Tests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the Ljung-Box tests for lags 1, 2, 3, 4, 5, 24, and 168 reject the assumption that the residual is not auto-correlated. The test result is supported by ACF, when missing values are dropped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/edxu96/Library/Caches/pypoetry/virtualenvs/elec-consumption-FzRBkitR-py3.7/lib/python3.7/site-packages/statsmodels/stats/diagnostic.py:559: FutureWarning: The value returned will change to a single DataFrame after 0.12 is released.  Set return_df to True to use to return a DataFrame now.  Set return_df to False to silence this warning.\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[True, True, True, True, True, True, True]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide\n",
    "[\n",
    "    pvalue < 0.05\n",
    "    for pvalue in sms.acorr_ljungbox(\n",
    "        data['zeroHourFbResid']['resid'].fillna(0), lags=[1, 2, 3, 4, 5, 24, 168]\n",
    "    )[1].tolist()\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 698.991x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#hide_input\n",
    "_, ax = plt.subplots(1, 1, figsize=(3 * _golden_ratio * 2, 3))\n",
    "plot_acf(data['zeroHourFbResid']['resid'].dropna(), lags=60, ax=ax);\n",
    "ax.set_ylim([-0.1, 0.4]);\n",
    "ax.set_xlim([0.5, 60.5]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seasonal ARIMA for Residuals\n",
    "\n",
    "Such information can be modelled by a SARIMA with AR1, AR1 (24), MA1 (24). Then, independence assumptions in lags 1, 2, 3, 4, and 5 are not rejected. So, the model has been improved, which is supported by the following ACF."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/edxu96/Library/Caches/pypoetry/virtualenvs/elec-consumption-FzRBkitR-py3.7/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning: No frequency information was provided, so inferred frequency H will be used.\n",
      "  % freq, ValueWarning)\n"
     ]
    }
   ],
   "source": [
    "#hide\n",
    "fit = ARIMA(\n",
    "    endog=data['zeroHourFbResid']['resid'].fillna(0), order=(1, 0, 0),\n",
    "    freq='H', seasonal_order=(1, 0, 1, 24), trend=None\n",
    ").fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 698.991x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#hide_input\n",
    "_, ax = plt.subplots(1, 1, figsize=(3 * _golden_ratio * 2, 3))\n",
    "plot_acf(fit.resid, lags=60, ax=ax);\n",
    "ax.set_ylim([-0.1, 0.1]);\n",
    "ax.set_xlim([0.5, 60.5]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/edxu96/Library/Caches/pypoetry/virtualenvs/elec-consumption-FzRBkitR-py3.7/lib/python3.7/site-packages/statsmodels/stats/diagnostic.py:559: FutureWarning: The value returned will change to a single DataFrame after 0.12 is released.  Set return_df to True to use to return a DataFrame now.  Set return_df to False to silence this warning.\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[False, False, False, False, False, True, True]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide\n",
    "[\n",
    "    pvalue < 0.05\n",
    "    for pvalue in sms.acorr_ljungbox(fit.resid, lags=[1, 2, 3, 4, 5, 24, 168])[1].tolist()\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, the model is still not satisfying, because it does not pass normality test and heteroskedasticity test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>                <td>resid</td>             <th>  No. Observations:  </th>   <td>2928</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>ARIMA(1, 0, 0)x(1, 0, [1], 24)</td> <th>  Log Likelihood     </th> <td>2740.060</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                   <td>Fri, 18 Dec 2020</td>        <th>  AIC                </th> <td>-5470.119</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                       <td>07:46:30</td>            <th>  BIC                </th> <td>-5440.209</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                    <td>08-01-2017</td>           <th>  HQIC               </th> <td>-5459.347</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                          <td>- 11-30-2017</td>          <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>               <td>opg</td>              <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>    <td> 4.445e-05</td> <td>    0.009</td> <td>    0.005</td> <td> 0.996</td> <td>   -0.017</td> <td>    0.017</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>    <td>    0.3254</td> <td>    0.008</td> <td>   40.291</td> <td> 0.000</td> <td>    0.310</td> <td>    0.341</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.S.L24</th> <td>    0.9902</td> <td>    0.004</td> <td>  236.527</td> <td> 0.000</td> <td>    0.982</td> <td>    0.998</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.S.L24</th> <td>   -0.9677</td> <td>    0.008</td> <td> -119.830</td> <td> 0.000</td> <td>   -0.984</td> <td>   -0.952</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>   <td>    0.0090</td> <td> 7.72e-05</td> <td>  116.406</td> <td> 0.000</td> <td>    0.009</td> <td>    0.009</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.52</td> <th>  Jarque-Bera (JB):  </th> <td>117272.85</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.47</td> <th>  Prob(JB):          </th>   <td>0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>1.77</td> <th>  Skew:              </th>   <td>4.07</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td> <th>  Kurtosis:          </th>   <td>32.92</td>  \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                     SARIMAX Results                                      \n",
       "==========================================================================================\n",
       "Dep. Variable:                              resid   No. Observations:                 2928\n",
       "Model:             ARIMA(1, 0, 0)x(1, 0, [1], 24)   Log Likelihood                2740.060\n",
       "Date:                            Fri, 18 Dec 2020   AIC                          -5470.119\n",
       "Time:                                    07:46:30   BIC                          -5440.209\n",
       "Sample:                                08-01-2017   HQIC                         -5459.347\n",
       "                                     - 11-30-2017                                         \n",
       "Covariance Type:                              opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const       4.445e-05      0.009      0.005      0.996      -0.017       0.017\n",
       "ar.L1          0.3254      0.008     40.291      0.000       0.310       0.341\n",
       "ar.S.L24       0.9902      0.004    236.527      0.000       0.982       0.998\n",
       "ma.S.L24      -0.9677      0.008   -119.830      0.000      -0.984      -0.952\n",
       "sigma2         0.0090   7.72e-05    116.406      0.000       0.009       0.009\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.52   Jarque-Bera (JB):            117272.85\n",
       "Prob(Q):                              0.47   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               1.77   Skew:                             4.07\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                        32.92\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide\n",
    "# The constant term should be removed.\n",
    "fit.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pseudo out-of-sample validations for `Prophet` and that with additional seasonal ARIMA have not been finished. So `Prophet` is used to make 4-step forecast."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Future Work\n",
    "\n",
    "- To combine multiple series belonging to different units is not a good idea, through there is a simple way to do it ([estimating same model over multiple time series, `crossvalidated`](https://stats.stackexchange.com/a/23123/231405)). As suggested before, there are trending components. Though they can be offset, such information is lost.\n",
    "- Apply more flexible methods and tune parameters using pseudo out-of-sample validation.\n",
    "- Generalized estimating equations (GEE) can be a next-stage model, because clustering results can be taken into consideration, though to apply GEE to time series is not easy. There are more training data when a general model for profiles in a whole cluster is to be built. Besides, scaling and translation variances should be removed first.\n",
    "- The fact that profiles are non-stationary because of weekends and holidays is challenging. It might help if same profile can be divided into two batches. Because the batch for such non-business-days has less observations, those from different households in the same cluster can be combined into one training set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Results\n",
    "\n",
    "Two sets of forecasts for 500 households are made. __Note that the physical unit is [presumed to be kiloWatt](https://edxu96.github.io/elec_consumption/#1.-Introduction).__\n",
    "\n",
    "- 3-step forecasts based on daily profile, done by integrated SARIMA (with AR1, MA1 and weekly seasonal AR1), in [a comma-separated CSV file](https://github.com/edxu96/elec_consumption/blob/master/results/three_step_daily.csv)\n",
    "- 4-step forecasts based on hourly profile, done by Facebook `Prophet`, in [another comma-separated CSV file](https://github.com/edxu96/elec_consumption/blob/master/results/four_step_hourly.csv)"
   ]
  }
 ],
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